{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Image manipulation detection by MVSS-Net\n",
    "\n",
    "This tutorial code provides a step-by-step demonstration of how to use a pre-trained MVSS-Net for detect manipulation in a given image. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Requirements and utils**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import io\n",
    "import torch\n",
    "import cv2\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from PIL import Image\n",
    "\n",
    "from models.mvssnet import get_mvss\n",
    "from models.resfcn import ResFCN\n",
    "from common.tools import inference_single\n",
    "from common.utils import calculate_pixel_f1\n",
    "from apex import amp\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Configs**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "fake_path = './data/demo/fake.jpg'\n",
    "fake_gt_path = './data/demo/fake_gt.png'\n",
    "real_path = './data/demo/real.jpg'\n",
    "model_type = 'mvssnet'\n",
    "mvssnet_path = './ckpt/mvssnet_casia.pt'\n",
    "resfcn_path = './ckpt/resfcn_casia.pt'\n",
    "resize = 512\n",
    "th = 0.5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Step 1. Load a pre-trained MVSS-Net**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load pretrain success\n",
      "----------use sobel-------------\n",
      "----------use constrain-------------\n",
      "load pretrain success\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "MVSSNet(\n",
       "  (model): ResNet(\n",
       "    (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
       "    (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    (relu): ReLU(inplace=True)\n",
       "    (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
       "    (layer1): Sequential(\n",
       "      (0): Bottleneck(\n",
       "        (conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU(inplace=True)\n",
       "        (downsample): Sequential(\n",
       "          (0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "      )\n",
       "      (1): Bottleneck(\n",
       "        (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU(inplace=True)\n",
       "      )\n",
       "      (2): Bottleneck(\n",
       "        (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU(inplace=True)\n",
       "      )\n",
       "    )\n",
       "    (layer2): Sequential(\n",
       "      (0): Bottleneck(\n",
       "        (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "        (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU(inplace=True)\n",
       "        (downsample): Sequential(\n",
       "          (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "          (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "      )\n",
       "      (1): Bottleneck(\n",
       "        (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU(inplace=True)\n",
       "      )\n",
       "      (2): Bottleneck(\n",
       "        (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU(inplace=True)\n",
       "      )\n",
       "      (3): Bottleneck(\n",
       "        (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU(inplace=True)\n",
       "      )\n",
       "    )\n",
       "    (layer3): Sequential(\n",
       "      (0): Bottleneck(\n",
       "        (conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU(inplace=True)\n",
       "        (downsample): Sequential(\n",
       "          (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "          (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "      )\n",
       "      (1): Bottleneck(\n",
       "        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU(inplace=True)\n",
       "      )\n",
       "      (2): Bottleneck(\n",
       "        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU(inplace=True)\n",
       "      )\n",
       "      (3): Bottleneck(\n",
       "        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU(inplace=True)\n",
       "      )\n",
       "      (4): Bottleneck(\n",
       "        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU(inplace=True)\n",
       "      )\n",
       "      (5): Bottleneck(\n",
       "        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU(inplace=True)\n",
       "      )\n",
       "    )\n",
       "    (layer4): Sequential(\n",
       "      (0): Bottleneck(\n",
       "        (conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU(inplace=True)\n",
       "        (downsample): Sequential(\n",
       "          (0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (1): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "      )\n",
       "      (1): Bottleneck(\n",
       "        (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False)\n",
       "        (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU(inplace=True)\n",
       "      )\n",
       "      (2): Bottleneck(\n",
       "        (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(4, 4), dilation=(4, 4), bias=False)\n",
       "        (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU(inplace=True)\n",
       "      )\n",
       "    )\n",
       "    (avgpool): AvgPool2d(kernel_size=7, stride=1, padding=0)\n",
       "    (fc): Linear(in_features=2048, out_features=1000, bias=True)\n",
       "  )\n",
       "  (relu): ReLU(inplace=True)\n",
       "  (upsample): Upsample(scale_factor=2.0, mode=bilinear)\n",
       "  (upsample_4): Upsample(scale_factor=4.0, mode=bilinear)\n",
       "  (erb_db_1): ERB(\n",
       "    (conv1): Conv2d(256, 1, kernel_size=(1, 1), stride=(1, 1))\n",
       "    (conv2): Conv2d(1, 1, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (relu): ReLU()\n",
       "    (bn): BatchNorm2d(1, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    (conv3): Conv2d(1, 1, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "  )\n",
       "  (erb_db_2): ERB(\n",
       "    (conv1): Conv2d(512, 1, kernel_size=(1, 1), stride=(1, 1))\n",
       "    (conv2): Conv2d(1, 1, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (relu): ReLU()\n",
       "    (bn): BatchNorm2d(1, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    (conv3): Conv2d(1, 1, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "  )\n",
       "  (erb_db_3): ERB(\n",
       "    (conv1): Conv2d(1024, 1, kernel_size=(1, 1), stride=(1, 1))\n",
       "    (conv2): Conv2d(1, 1, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (relu): ReLU()\n",
       "    (bn): BatchNorm2d(1, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    (conv3): Conv2d(1, 1, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "  )\n",
       "  (erb_db_4): ERB(\n",
       "    (conv1): Conv2d(2048, 1, kernel_size=(1, 1), stride=(1, 1))\n",
       "    (conv2): Conv2d(1, 1, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (relu): ReLU()\n",
       "    (bn): BatchNorm2d(1, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    (conv3): Conv2d(1, 1, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "  )\n",
       "  (erb_trans_1): ERB(\n",
       "    (conv1): Conv2d(1, 1, kernel_size=(1, 1), stride=(1, 1))\n",
       "    (conv2): Conv2d(1, 1, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (relu): ReLU()\n",
       "    (bn): BatchNorm2d(1, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    (conv3): Conv2d(1, 1, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "  )\n",
       "  (erb_trans_2): ERB(\n",
       "    (conv1): Conv2d(1, 1, kernel_size=(1, 1), stride=(1, 1))\n",
       "    (conv2): Conv2d(1, 1, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (relu): ReLU()\n",
       "    (bn): BatchNorm2d(1, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    (conv3): Conv2d(1, 1, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "  )\n",
       "  (erb_trans_3): ERB(\n",
       "    (conv1): Conv2d(1, 1, kernel_size=(1, 1), stride=(1, 1))\n",
       "    (conv2): Conv2d(1, 1, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (relu): ReLU()\n",
       "    (bn): BatchNorm2d(1, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    (conv3): Conv2d(1, 1, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "  )\n",
       "  (sobel_x1): Sequential(\n",
       "    (0): Conv2d(256, 1, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "    (1): BatchNorm2d(1, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  )\n",
       "  (sobel_y1): Sequential(\n",
       "    (0): Conv2d(256, 1, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "    (1): BatchNorm2d(1, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  )\n",
       "  (sobel_x2): Sequential(\n",
       "    (0): Conv2d(512, 1, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "    (1): BatchNorm2d(1, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  )\n",
       "  (sobel_y2): Sequential(\n",
       "    (0): Conv2d(512, 1, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "    (1): BatchNorm2d(1, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  )\n",
       "  (sobel_x3): Sequential(\n",
       "    (0): Conv2d(1024, 1, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "    (1): BatchNorm2d(1, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  )\n",
       "  (sobel_y3): Sequential(\n",
       "    (0): Conv2d(1024, 1, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "    (1): BatchNorm2d(1, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  )\n",
       "  (sobel_x4): Sequential(\n",
       "    (0): Conv2d(2048, 1, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "    (1): BatchNorm2d(1, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  )\n",
       "  (sobel_y4): Sequential(\n",
       "    (0): Conv2d(2048, 1, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "    (1): BatchNorm2d(1, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  )\n",
       "  (res50): ResNet50(\n",
       "    (model): ResNet(\n",
       "      (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
       "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "      (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
       "      (layer1): Sequential(\n",
       "        (0): Bottleneck(\n",
       "          (conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu): ReLU(inplace=True)\n",
       "          (downsample): Sequential(\n",
       "            (0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          )\n",
       "        )\n",
       "        (1): Bottleneck(\n",
       "          (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu): ReLU(inplace=True)\n",
       "        )\n",
       "        (2): Bottleneck(\n",
       "          (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu): ReLU(inplace=True)\n",
       "        )\n",
       "      )\n",
       "      (layer2): Sequential(\n",
       "        (0): Bottleneck(\n",
       "          (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "          (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu): ReLU(inplace=True)\n",
       "          (downsample): Sequential(\n",
       "            (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "            (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          )\n",
       "        )\n",
       "        (1): Bottleneck(\n",
       "          (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu): ReLU(inplace=True)\n",
       "        )\n",
       "        (2): Bottleneck(\n",
       "          (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu): ReLU(inplace=True)\n",
       "        )\n",
       "        (3): Bottleneck(\n",
       "          (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu): ReLU(inplace=True)\n",
       "        )\n",
       "      )\n",
       "      (layer3): Sequential(\n",
       "        (0): Bottleneck(\n",
       "          (conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "          (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu): ReLU(inplace=True)\n",
       "          (downsample): Sequential(\n",
       "            (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "            (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          )\n",
       "        )\n",
       "        (1): Bottleneck(\n",
       "          (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu): ReLU(inplace=True)\n",
       "        )\n",
       "        (2): Bottleneck(\n",
       "          (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu): ReLU(inplace=True)\n",
       "        )\n",
       "        (3): Bottleneck(\n",
       "          (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu): ReLU(inplace=True)\n",
       "        )\n",
       "        (4): Bottleneck(\n",
       "          (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu): ReLU(inplace=True)\n",
       "        )\n",
       "        (5): Bottleneck(\n",
       "          (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu): ReLU(inplace=True)\n",
       "        )\n",
       "      )\n",
       "      (layer4): Sequential(\n",
       "        (0): Bottleneck(\n",
       "          (conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu): ReLU(inplace=True)\n",
       "          (downsample): Sequential(\n",
       "            (0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (1): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          )\n",
       "        )\n",
       "        (1): Bottleneck(\n",
       "          (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False)\n",
       "          (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu): ReLU(inplace=True)\n",
       "        )\n",
       "        (2): Bottleneck(\n",
       "          (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(4, 4), dilation=(4, 4), bias=False)\n",
       "          (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu): ReLU(inplace=True)\n",
       "        )\n",
       "      )\n",
       "      (avgpool): AvgPool2d(kernel_size=7, stride=1, padding=0)\n",
       "      (fc): Linear(in_features=2048, out_features=1000, bias=True)\n",
       "    )\n",
       "    (relu): ReLU(inplace=True)\n",
       "  )\n",
       "  (constrain_conv): BayarConv2d()\n",
       "  (head): _DAHead(\n",
       "    (conv_p1): Sequential(\n",
       "      (0): Conv2d(4096, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (2): ReLU(inplace=True)\n",
       "    )\n",
       "    (conv_c1): Sequential(\n",
       "      (0): Conv2d(4096, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (2): ReLU(inplace=True)\n",
       "    )\n",
       "    (pam): _PositionAttentionModule(\n",
       "      (conv_b): Conv2d(1024, 128, kernel_size=(1, 1), stride=(1, 1))\n",
       "      (conv_c): Conv2d(1024, 128, kernel_size=(1, 1), stride=(1, 1))\n",
       "      (conv_d): Conv2d(1024, 1024, kernel_size=(1, 1), stride=(1, 1))\n",
       "      (softmax): Softmax(dim=-1)\n",
       "    )\n",
       "    (cam): _ChannelAttentionModule(\n",
       "      (softmax): Softmax(dim=-1)\n",
       "    )\n",
       "    (conv_p2): Sequential(\n",
       "      (0): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (2): ReLU(inplace=True)\n",
       "    )\n",
       "    (conv_c2): Sequential(\n",
       "      (0): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (2): ReLU(inplace=True)\n",
       "    )\n",
       "    (out): Sequential(\n",
       "      (0): Dropout(p=0.1, inplace=False)\n",
       "      (1): Conv2d(1024, 1, kernel_size=(1, 1), stride=(1, 1))\n",
       "    )\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "if model_type == 'mvssnet':\n",
    "    model = get_mvss(backbone='resnet50',\n",
    "                     pretrained_base=True,\n",
    "                     nclass=1,\n",
    "                     sobel=True,\n",
    "                     constrain=True,\n",
    "                     n_input=3,\n",
    "                     )\n",
    "    checkpoint = torch.load(mvssnet_path, map_location='cpu')\n",
    "elif model_type == 'fcn': \n",
    "    model = ResFCN()\n",
    "    checkpoint = torch.load(resfcn_path, map_location='cpu')\n",
    "\n",
    "model.load_state_dict(checkpoint, strict=True)\n",
    "model.cuda()\n",
    "model.eval()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Step 2. Load fake and real images**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=384x256 at 0x7FDD839512D0>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fake = cv2.imread(fake_path)\n",
    "fake_size = fake.shape\n",
    "fake_ = cv2.resize(fake, (resize, resize))\n",
    "Image.fromarray(fake[:,:,::-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=384x256 at 0x7FDD7C35A150>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "real = cv2.imread(real_path)\n",
    "real_size = real.shape\n",
    "real_ = cv2.resize(real, (resize, resize))\n",
    "Image.fromarray(real[:,:,::-1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Step 3. Try to detect manipulation**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "with torch.no_grad():\n",
    "    fake_seg, _ = inference_single(img=fake_, model=model, th=0)\n",
    "    fake_seg = cv2.resize(fake_seg, (fake_size[1], fake_size[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "with torch.no_grad():\n",
    "    real_seg, _ = inference_single(img=real_, model=model, th=0)\n",
    "    real_seg = cv2.resize(real_seg.astype(np.uint8), (real_size[1], real_size[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Step 4. Visualize detection results**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<PIL.Image.Image image mode=L size=384x256 at 0x7FDD7C35FB90>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image.fromarray((fake_seg).astype(np.uint8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<PIL.Image.Image image mode=L size=384x256 at 0x7FE0E961B090>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image.fromarray((real_seg).astype(np.uint8))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Step 5. Quantitative evaluation (only manipulated image)**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fake_img, pixel_f1: 0.6149, p: 0.4448, r: 0.9960\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<PIL.Image.Image image mode=L size=384x256 at 0x7FE0E45289D0>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "if os.path.exists(fake_gt_path):\n",
    "    fake_gt = cv2.imread(fake_gt_path, 0) / 255.0\n",
    "    f1, p, r = calculate_pixel_f1(fake_seg.flatten(),fake_gt.flatten())\n",
    "    print(\"fake_img, pixel_f1: %.4f, p: %.4f, r: %.4f\"%(f1, p, r))\n",
    "else:\n",
    "    print(\"groundtruth: %s not exists.\"%fake_gt_path)\n",
    "Image.fromarray((fake_gt * 255).astype(np.uint8))"
   ]
  }
 ],
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